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2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022

作者： Kumar, Dayanand Huang, Yi-Rong Pal, Pratibha Saleem, Aftab Singh, Amit Lee, Hoonkyung Wang, Yeong-Her Tseng, Tseung Yuen National Cheng-Kung University Institute of Microelectronics Department of Electrical Engineering Tainan701 Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu30010 Taiwan National Cheng Kung University Department of Materials Science and Engineering Tainan70101 Taiwan National Yang Ming Chiao Tung University Department of Electrical Engineering and Computer Science 30010 Taiwan Research Center for Applied Sciences Academia Sinica Taipei11529 Taiwan Konkuk University Department of Physics Seoul05029 Korea Republic of

ISBN: (纸本)9781665409230

We present highly stable bilayer HfO2/TiO2 memristive synapse for neuromorphic computing applications. The memristive synapse shows repetitive 220 Long-term potentiation and depression cycles without any breakdown with total number of 220k pulses. The nonlinear values of potentiation and depression are 2.52 and -2.63, respectively with 1000 conductance pulses (500 pulses of potentiation and 500 pulses of depression). The experimental data of potentiation and depression was used to train HNN for pattern recognition of 28×28 pixels comprising 784 synapses. In 16 iterations, the HNN can be successfully trained to identify the input image with a training accuracy of about 97%. Moreover, the device has good retention (104 s) at 120 °C. These excellent synaptic characteristics of annealed device allows it for artificial intelligence systems in near future. © 2022 IEEE.

关键词： Hafnium oxides

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Quantized Wavelet Dilated Convolutional Neural Network Based Intelligent Surveillance System for Public Safety in IoT Security Networks

Quantized Wavelet Dilated Convolutional Neural Network Based...

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Sustainable Expert Systems (ICSES), International Conference on

作者： R. Sowmya Ruchira Rawat Dinesh Mishra Mamtaben Kamleshbhai Megha Monikaben Maheshbhai Patel Sathish Kannan Department of CSE (AIML) S. A. Engineering College Chennai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Mangalayatan University-Jabalpur Jabalpur Madhya Pradesh India Department of Computer Science Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of CVM University Anand Gujarat India Department of Computer Science Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of CVM University Anand Gujarat India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Smart cities have produced vast amounts of surveillance video data that can be processed to identify anomalies since the introduction of visual sensors. A novel Quantized Wavelet Auxiliary Encoder Self-Attention Dilated Convolutional Neural Network with Gold Rush Optimizer (QWAESDCNN-GRO) designed to enhance security and reduce crime potential. Through the integration of the Quantized Discrete Haar Wavelet Transform (QDHWT), frame-level features have been extracted. Subsequently, the recovered features are fed to the Auxiliary Guided Knowledge Encoder-Decoder (AGED), which uses them to rank the features. These features are then sent to the Self-Attention Causal Dilated Convolutional Neural Network (SACDCNN) with Gold Rush Optimization (GRO) for anomaly detection. Performance indicators including Fl-score, recall, accuracy, precision, and AUC are compared with those of earlier techniques in order to assess the effectiveness of the recommended strategy. Using the UCF-Crime dataset, the model achieved an accuracy of 99.87% and an error rate of 0.021%. On the Hockey Fights dataset, it obtained an accuracy of 99.88% and an error rate of 0.019%.

关键词： Gold Accuracy Error analysis Surveillance Feature extraction Discrete wavelet transforms Convolutional neural networks Security Optimization Sports

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A Matching Based Escape Routing Algorithm With Variable Design Rules and Multiple Constraints

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Chen, Chuandong Lin, Disi Liu, Qinghai He, Huan Lin, Zhifeng Liu, Genggeng Chen, Jianli Chang, Yao-Wen Fuzhou University Department of Microelectronics Fuzhou350108 China Fuzhou University Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China Center for Applied Mathematics of Fujian Province Center for Discrete Mathematics Fuzhou350108 China Hangzhou Huawei Enterprises Telecommunication Technologies Co. Ltd Hangzhou310000 China Fuzhou University Center for Discrete Mathematics and Theoretical Computer Science Fuzhou350108 China Fuzhou University College of Computer and Data Science Fuzhou350108 China Fudan University School of Microelectronics Shanghai200433 China National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Taipei106 Taiwan

Escape routing is a critical problem in PCB routing, and its quality dramatically affects the cost of the PCB design. Unlike the traditional escape routing that works mainly for the BGA with unique line width and space, this paper presents a high-performance escape routing algorithm to handle problems with variable design rules and multiple constraints. We first propose a novel obstacle-avoiding method to project pins to the boundary and construct a channel projection graph combined with a channel merging technique to handle complex irregular packages. We then construct a bi-projection graph and propose a matching-based hierarchical sequencing algorithm to consider manual constraints. We perform global routing for each pin/differential pair by congestion-avoiding path initializing and rip-up and reroute path optimizing. Finally, a length-aware detail routing algorithm is developed to optimize the line length while ensuring the differential pair constraints. The experimental results on industrial PCB instances show that our algorithm can achieve 100% routability without violating the design rules and constraints, while two state-of-the-art PCB routers, FreeRouting and Allegro, cannot complete escape routing. © 1982-2012 IEEE.

关键词： Printed circuit design

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AI-based Object Detection for Assisting the Visually Impaired People

AI-based Object Detection for Assisting the Visually Impaire...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Syed Sameer Parul Madan Sathish Kannan Vijay Jagdish Upadhye Harshal Patil S. Rajkumar Project Engineer Department of Science and Technology Karnataka State Council for Science and Technology (KSCST) Indian Institute of Science (IISc) Campus Bengaluru Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Deputy Director Research and Development Cell Department of Microbiology Parul Institute of Applied Sciences Parul University Waghodia Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Mechanical Engineering Faculty of Manufacturing Institute of Technology Hawassa University Ethiopia

In recent decades, vision problems have become more common. One significant barrier is that people who are visually impaired generally require assistance performing their daily tasks. As a result, developing a technological solution to this challenge is critical. Implementing an automatic object identification system can enable visually impaired people to roam independently. The study employs two Artificial Intelligence (AI) models, a Region-based Fully Convolutional Network (RFCN) and a Mask Region-based Convolutional Neural Network (Mask RCNN), to detect objects and assess their performance. The evaluation demonstrates RFCN's outstanding performance with an impressive Mean Average Precision (mAP) score of 0.825. The Raspberry Pi processor is used as a vital component in the process of hardware development. The RFCN model is integrated into the processor to aid in the identification of objects collected by the system's pi-camera. The detected objects are communicated to users via auditory feedback. The hardware system also includes an ultrasonic sensor, which provides distance information for the detected objects. This full hardware solution is specifically developed to help visually impaired people overcome mobility issues in both indoor and outdoor settings. The suggested system seeks to improve the autonomy and safety of visually impaired individuals in their day-to-day activities by seamlessly mixing advanced technologies such as AI and embedded systems.

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Computational Model Based on CNN to Identify Masses from Liver Images

Computational Model Based on CNN to Identify Masses from Liv...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： CH. N. V. Jyothirmai G.R. Hemalakshmi N.B. Prakash Vijay Jagdish Upadhye Harshal Patil Syed Noeman Taqui Department of CSE Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering & Technology Andhra Pradesh India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electrical and Electronics Engineering National Engineering College Kovilpatti India Department of Microbiology Research and Development Cell Parul Institute of Applied Sciences Parul University Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Maharashtra India Department of VLSI Microelectronics. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Therefore, Convolutional Neural Networks (CNNs) are capable of predicting or extrapolating from identified patterns to unknown data, enabling the analysis and extraction of knowledge from large images, in order to produce consistent outputs for inputs not previously presented. On the downside, the training process takes a long time as it is computationally expensive. Aiming to reduce the computational time to perform the training of the neural network (NN), and the analysis of images during the testing process, as well as taking advantage of the natural distributed characteristic of ANN, it was decided to transfer the processing-intensive parts of the tool from CPU (Central Processing Unit) to a GPU (Graphics Processing Unit). Finally, simulator tests were carried out, with consequent verification of the effectiveness of diagnoses on ultrasound images of the liver. The accuracy and total processing time for carrying out the analyses proposed during this research were then evaluated, making a comparison between the network training and testing times obtained on the CPU and GPU. It can also be seen - in both Tr&Tt cases - that GPU had a higher gain than CPU (about 10.6 times faster), even when dealing with a small database, with 120 images of 202500 pixels each.

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The Future of EV: Real-Time Development of an Intelligent Wireless Charging System for Electric Vehicles

The Future of EV: Real-Time Development of an Intelligent Wi...

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Cognitive, Green and Ubiquitous Computing (IC-CGU), International Conference on

作者： A. Rajalingam Darwin Nesakumar A S. Sathya Jishor Erat Krishnankutty Abid Y Jubert R. Pagunuran Engineering Department College of Engineering and Technology University of Technology and Applied Sciences Shinas Sultanate of Oman Department of Electronics and Communication Engineering R. M. K. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha School of Engineering Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

The world is always on the lookout for new ways to put technology to use, and it is changing at a rate no one has ever seen before. For decades, the car industry has done nothing to alter. The advent of electric vehicles, which run on electricity rather than fossil fuels, was the big breakthrough. There have been many investigations and appreciated structures as well as outcomes for wirelessly powering electric automobiles, both passively and rapidly. This might result in a more rapid acceptance of electric vehicles globally, as it is an emerging technology in automotive manufacturing that has not yet been widely used. We cross-validated our Intelligent Wireless Charging System (IWCS), a wireless power transmission charging circuit for electric vehicles, with the Classical Charging System (CCS) to assess the efficacy of the proposed model. To solve the problem of battery overheating during charging while plugged in, as well as to extend the vehicle's battery life, the suggested model is employed. Batteries temperature handling is the primary concern for EVs due to the increased heat losses while plugged-in charging, which in turn reduces the battery's lifespan. Currently, electric cars mostly employ water-cooled systems to cool the battery, which isn't ideal for EVs due to the added size and weight. Air cooling is another option for cooling systems; it's already used in internal combustion engines but isn't very effective in electric vehicles since charging the battery produces too much heat. There is a considerable decrease in thermal output when we employ wireless charging technology. The proposed system's primary objective is to provide wireless power transfer using inductive coupling as its basis.

关键词： Coils Wireless communication Industries Cooling Inductive charging Wireless power transfer Electric vehicles

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Hot Carrier Injection Reliability of Fabricated N- and P-Type Multi FinFETs with Different TiN Stacks

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ECS Journal of Solid State Science and Technology 2023年第3期12卷 035007-035007页

作者： Chen, Yu-Lin Yeh, Wen-Kuan Hsu, Heng-Tung Chen, Ke-Horng Lin, Wen-Chin Yu, Tien-Han Chou, Hung-Ting Godwin Raj, D. Godfrey, D. International College of Semiconductor Technology National Yang Ming Chiao Tung University Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan National Applied Research Laboratories Taiwan Institute of Microelectronics National Cheng Kung University Taiwan National Chung-Shan Institute of Science and Technology Taoyuan Taiwan Dept of ECE Amal Jyothi College of Engineering Kerala India Dept of Electronics and Communication Engineering Dayananda Sagar University Bengaluru India

Device degradation due to hot carrier injection (HCI) in multi-fin 20 nm and 10 nm N- and P-type FinFET devices are thoroughly analyzed. To further understand the HCI reliability of the four FinFET devices, the device is fabricated with a standard Vt base and low Vt base gate stacks with different work functions. It is evident that: (i) The standard Vt device sustains lower effective stress bias due to the difference in threshold voltage, resulting in a more stable threshold voltage than the low Vt base device, and (ii) the transconductance of the single N- and P-type FinFET is more severely degraded than the multi-fin N- and P-type FinFET, mainly because multi N- and P-type Finfet has coupe effect, which effectively reduces the impact of HCI. © 2023 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited

关键词： FinFET

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Work-in-Progress: Towards an International Data Spaces Connector for the Internet of Things

Work-in-Progress: Towards an International Data Spaces Conne...

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IEEE International Workshop on Factory Communication Systems (WFCS)

作者： Michael Nast Benjamin Rother Frank Golatowski Dirk Timmermann Jens Leveling Christian Olms Christian Nissen Institute of Applied Microelectronics and Computer Engineering University of Rostock Rostock Germany Software and Information Engineering Fraunhofer Institute for Material Flow and Logistics Dortmund Germany

ISBN: (数字)9781728152974

ISBN: (纸本)9781728152981

In the age of digitalization, data protection plays an important role. Data is created, modified and shared over the Internet between data owners and data users. A key issue in this context is the respect of data sovereignty. The International Data Spaces (IDS) Reference Architecture has been developed in order to preserve data sovereignty. In this regard, Internet of Things (IoT) devices, such as sensors, play an important role for providing data. However, an IoT device is not capable of being integrated directly into the IDS by default. We propose an approach to enable the IDS for vendor independent IoT devices, using an open interoperability standard for the IoT specified by the Open Geospatial Consortium (OGC). This allows data owners to benefit from providing their own data while retaining control over it.

关键词： International Data Spaces (IDS) internet of Things (IoT) open Geospatial Consortium (OGC) SensorThings API message Queuing Telemetry Transport (MQTT) microservices Internet of things intelligent decision support system Connector Device Component Internets Thing International(headers)

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Attention-based Skin Cancer Classification Through Hyperspectral Imaging

Attention-based Skin Cancer Classification Through Hyperspec...

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Euromicro Symposium on Digital System Design

作者： Marco La Salvia Emanuele Torti Marco Gazzoni Elisa Marenzi Raquel Leon Samuel Ortega Himar Fabelo Gustavo M. Callico Francesco Leporati Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Institute for Applied Microelectronics (IUMA) Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Spain

ISBN: (数字)9781665474047

ISBN: (纸本)9781665474054

In recent years, hyperspectral imaging has been employed in several medical applications, targeting automatic diagnosis of different diseases. These images showed good performance in identifying different types of cancers. Among the methods used for classification, machine learning and deep learning techniques emerged as the most suitable algorithms to handle these data. In this paper, we propose a novel hyperspectral image classification architecture exploiting Vision Transformers. We validated the method on a real hyperspectral dataset containing 76 skin cancer images. Obtained results clearly highlight that the Vision Transforms are a suitable architecture for this task. Measured results outperform the state-of-the-art both in terms of false negative rates and of processing times. Finally, the attention mechanism is evaluated for the first time on medical hyperspectral images.

关键词： Training Runtime Transforms Transformers Time measurement Task analysis Medical diagnostic imaging

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Use of assisted reproductive technologies for male and female infertility and perinatal outcomes

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Fertility and Sterility 2025年

作者： Magnus, Maria C. Skåra, Karoline H. Carlsen, Ellen Ø. Gjerdevik, Miriam Ramlau-Hansen, Cecilia H. Myrskylä, Mikko Romundstad, Liv-Bente Håberg, Siri E. Centre for Fertility and Health Norwegian Institute of Public Health Oslo Norway Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Public Health Research Unit for Epidemiology Aarhus University Aarhus Denmark Max Planck Institute for Demographic Research Rostock Germany Helsinki Institute for Demography and Population Health University of Helsinki Helsinki Finland Max Planck-University of Helsinki Center for Social Inequalities in Population Health Rostock Germany Spiren Fertility Clinic Trondheim Norway Department of Global Public Health and Primary Care University of Bergen Bergen Norway

Objective: To compare adverse perinatal outcomes between naturally conceived and pregnancies conceived by assisted reproductive technologies (ARTs) according to underlying male, female, or unexplained infertility. Design: Registry-based study. Subjects: All singleton births in Norway between 2000 and 2021 recorded in the national birth registry. Exposure: We compared ART pregnancies with underlying male (N = 9,957), female (N = 10,031), mixed (N = 3,287), or unexplained (N = 7,178) infertility with naturally conceived pregnancies (N = 1,210,709). Information on underlying causes of infertility was reported from fertility clinics. Main Outcomes and Measures: We compared the birthweight and pregnancy length using robust linear regression, and the risk of pre-eclampsia, cesarean section, stillbirth, preterm birth, low birth weight, small-for-gestational age and transfer to neonatal unit using logistic regression, adjusting for parental age, maternal parity, cohabitation, country of birth, and year of delivery. Results: Pregnancy length and birthweight were lower in all ART groups compared with the naturally conceived. The shorter pregnancy length was less pronounced in ART deliveries resulting from male infertility (adjusted difference −1.24 days;95% confidence interval [CI]: −1.43 to −1.05) than in ART deliveries resulting from female infertility (adjusted difference −1.92 days;95% CI: −2.12 to −1.73). Similarly, the lower birthweight in ART deliveries was less pronounced in those resulting from male (adjusted difference −29 grams;95% CI: −39 to −18) than those resulting from female (adjusted difference −49 grams;95% CI: −59 to −39) infertility. We also observed a higher risk of most adverse perinatal outcomes in all ART groups, with the magnitude being lower for ART deliveries resulting from male infertility. Conclusion: The increased risk of adverse perinatal outcomes in pregnancies conceived by ART was less pronounced if used for male infertility than for female in

关键词： Assisted reproductive technologies infertility perinatal outcomes

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